Improved Flexibility and Interpretability of Large Vessel Stroke Prognostication Using Image Synthesis and Multi-task Learning

While acute ischemic stroke due to large vessel occlusion (LVO) may be life-threatening or permanently disabling, timely intervention with endovascular thrombectomy (EVT) can prove life-saving for affected patients. Appropriate patient selection based on prognostic prediction is vital for this costly and invasive procedure, as not all patients will benefit from EVT. Accurate prognostic prediction for LVO presents a significant challenge. Computed Tomography Perfusion (CTP) maps can provide additional information for clinicians to make decisions. However, CTP maps are not always available due to variations in available equipment, funding, expertise and image quality. To address these gaps, we test (i) the utility of acquired CTP maps in a deep learning prediction model, (ii) the ability to improve flexibility of this model through image synthesis, and (iii) the added benefits of including multi-task learning with a simple clinical task to focus the synthesis on key clinical features. Our results demonstrate that network architectures utilising a full set of images can still be flexibly deployed if CTP maps are unavailable as their benefits can be effectively synthesized from more widely available images (NCCT and CTA). Additionally, such synthesized images may help with interpretability and building a clinically trusted model.